feat(sklearn): refuse the label column as an input column

common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/machineLearning/sklearnAdvanced/base/SklearnAdvancedBaseDesc.scala

@@ -163,6 +163,17 @@ abstract class SklearnMLOperatorDescriptor[T <: ParamClass] extends PythonOperat
   override def getOutputSchemas(
       inputSchemas: Map[PortIdentity, Schema]
   ): Map[PortIdentity, Schema] = {
+    // The features go to `fit` as they are named, so the ground truth among them
+    // is the answer handed to the estimator as an input. Nothing fails when that
+    // happens, which is why it is refused here: the run finishes and the model
+    // scores far better than what it learned deserves.
+    if (Option(selectedFeatures).exists(_.contains(groundTruthAttribute))) {
+      throw new RuntimeException(
+        s""""$groundTruthAttribute" is the Ground Truth Attribute Column, so it cannot""" +
+          " also be a Selected Feature. Remove it from Selected Features, or fit against" +
+          " a different column."
+      )
+    }
     val outputSchema = Schema(
       List(
         new Attribute("Model", AttributeType.BINARY),

common/workflow-operator/src/main/scala/org/apache/texera/amber/operator/sklearn/SklearnModelOpDesc.scala

@@ -137,6 +137,16 @@ abstract class SklearnModelOpDesc extends PythonOperatorDescriptor {
             s" produces. Turn Count Vectorizer off, or use $alternatives."
         )
       }
+      // The generated code drops the target before the text pipeline reads its
+      // columns, so naming it here asks the pipeline for a column that is no
+      // longer there. Refused rather than vectorized: the label is the answer,
+      // and a model given it as a feature reads that answer off its own input.
+      if (text.contains(target)) {
+        throw new RuntimeException(
+          s""""$target" is the Target Attribute, so it cannot also be a Text Attribute.""" +
+            " Remove it from Text Attribute, or fit against a different column."
+        )
+      }
     }
     Map(
       operatorInfo.outputPorts.head.id -> Schema()

common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/machineLearning/sklearnAdvanced/base/SklearnMLOperatorDescriptorSpec.scala

@@ -62,6 +62,23 @@ class SklearnMLOperatorDescriptorSpec extends AnyFlatSpec with Matchers {
     )
   }
 
+  it should "reject the ground truth column named as a feature" in {
+    val op = newOp()
+    op.groundTruthAttribute = "species"
+    op.selectedFeatures = List("petal_length", "species")
+    val thrown = intercept[RuntimeException](op.getOutputSchemas(Map.empty))
+    thrown.getMessage should include("species")
+    thrown.getMessage should include("Ground Truth Attribute Column")
+    thrown.getMessage should include("Selected Features")
+  }
+
+  it should "let the features through while none of them is the ground truth" in {
+    val op = newOp()
+    op.groundTruthAttribute = "species"
+    op.selectedFeatures = List("petal_length", "petal_width")
+    op.getOutputSchemas(Map.empty).keySet shouldBe Set(op.operatorInfo.outputPorts.head.id)
+  }
+
   "SklearnMLOperatorDescriptor" should
     "default paraList to empty, groundTruthAttribute to empty, and selectedFeatures to null" in {
     val op = newOp()

common/workflow-operator/src/test/scala/org/apache/texera/amber/operator/sklearn/SklearnModelOpDescSpec.scala

@@ -103,4 +103,32 @@ class SklearnModelOpDescSpec extends AnyFlatSpec with Matchers {
     }
     d.getOutputSchemas(Map.empty).keySet shouldBe Set(d.operatorInfo.outputPorts.head.id)
   }
+
+  it should "reject the target named as a text column" in {
+    val d = new TestSklearnModelOpDesc
+    d.countVectorizer = true
+    d.target = "species"
+    d.text = List("note", "species")
+    val thrown = intercept[RuntimeException](d.getOutputSchemas(Map.empty))
+    thrown.getMessage should include("species")
+    thrown.getMessage should include("Target Attribute")
+    thrown.getMessage should include("Text Attribute")
+  }
+
+  it should "let the text columns through while none of them is the target" in {
+    val d = new TestSklearnModelOpDesc
+    d.countVectorizer = true
+    d.target = "species"
+    d.text = List("note")
+    d.getOutputSchemas(Map.empty).keySet shouldBe Set(d.operatorInfo.outputPorts.head.id)
+  }
+
+  it should "leave a stale text column alone while Count Vectorizer is off" in {
+    // Nothing reads `text` with the switch off, and the panel hides it, so a value
+    // left behind by an earlier configuration must not report the operator invalid.
+    val d = new TestSklearnModelOpDesc
+    d.target = "species"
+    d.text = List("species")
+    d.getOutputSchemas(Map.empty).keySet shouldBe Set(d.operatorInfo.outputPorts.head.id)
+  }
 }